CN113836716A - Health parameter grading fusion diagnosis method and system for thermal control system of complex spacecraft - Google Patents
Health parameter grading fusion diagnosis method and system for thermal control system of complex spacecraft Download PDFInfo
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Abstract
The invention belongs to the technical field of spacecraft health diagnosis and analysis, and particularly relates to a method and a system for diagnosing health parameters of a thermal control system of a complex spacecraft in a grading fusion mode. The diagnostic method comprises: constructing a multi-level evaluation parameter model of the thermal control system to monitor and jointly diagnose key index parameters from three aspects of single machine design key indexes, single machine equipment health representation and system level comprehensive state representation; designing a thermodynamic extreme working condition test data boundary model; constructing a single-index parameter long-term state monitoring curve analysis model and a single-index parameter characteristic model; and constructing a correlation characteristic model of the multi-index parameters in a space flight scene, and providing interpretation analysis and consistency comparison data for the multi-index, single-machine comprehensive state and system-level comprehensive state. The method can realize comprehensive credible evaluation of the associated core indexes in a single index-single machine-system level, and also can provide a discrimination method for intelligently identifying the abnormal state in the flight scene.
Description
Technical Field
The invention belongs to the technical field of spacecraft health diagnosis and analysis, and particularly relates to a method and a system for diagnosing health parameters of a thermal control system of a complex spacecraft in a grading fusion mode.
Background
With the technical progress in the fields of aerospace, aviation and the like in China, the requirement of complex spacecrafts such as spacecrafts, space stations and the like for long service life and high reliability is provided, the original design, test and operation management concept cannot meet the requirement of long service life, safe and reliable operation, the fault monitoring and evaluation key technology of the complex spacecrafts such as the space stations and the like is urgently needed to be researched as soon as possible, tasks such as autonomous perception, diagnosis, decision and execution are gradually realized, the fault diagnosis and evaluation efficiency is improved, the operation safety and reliability of the space stations are improved, the adaptability of military products and the operation analysis capability of combat spaces are enhanced, and the military application requirement in the future is met.
The thermal control system is used as a key component of the space station, and not only needs to ensure that the thermal control system can achieve the overall required performance and function, namely that structural components and instruments of the thermal control system are in a proper temperature range in a space environment and can normally work, but also needs to ensure that a sealed cabin where a spaceman is located meets certain temperature and humidity conditions and the flowing speed of gas in the cabin. Most of the current fault monitoring and evaluating means of the space station thermal control system can only carry out post analysis on shallow test data, and does not have the capability of deeply mining deep cause and effect and logic relationships represented behind the data, so that unified management and utilization of extreme test methods, test and test processes and conclusion data such as thermal vacuum are lacked, and a lot of precious experience information is lost. Meanwhile, due to the fact that the test experiment process and data cannot be fully utilized, unpredictable problems are likely to occur in the development and application processes, and development progress and development quality control are seriously affected.
Disclosure of Invention
Aiming at the problems, the invention designs a hierarchical fusion diagnosis method for health parameters of a thermal control system of a complex spacecraft, which constructs a system-level, single-level and index-level three-level health state evaluation parameter architecture according to constraint conditions such as design contents of the thermal control system, an on-orbit environment influence model, a flight scene and the like, designs a boundary condition model of an extreme thermal environment and a fault model of a typical component under a space flight scene, and realizes periodic and normalized analysis of indexes and state confirmation and evaluation of multi-level objects on the basis of a monitoring grammar enhancement technology and a state inference machine method.
Specifically, the invention provides a health parameter grading fusion diagnosis method for a thermal control system of a complex spacecraft, which comprises the following steps:
s1: constructing a multi-level parameter evaluation model of the thermal control system, and designing long-term monitoring and joint diagnosis of multi-level evaluation indexes from three aspects of single-machine key indexes, single-machine equipment health indexes and system-level comprehensive health state indexes;
s2: designing a thermodynamic extreme working condition test data boundary model for the constructed multi-level parameter evaluation model;
s3: constructing a single-index parameter long-term state monitoring curve analysis model and a single-index parameter characteristic model;
s4: and (3) constructing a characteristic correlation model of the multi-element index parameters in the flight scene, and providing an interpretation analysis method after multi-element parameter fusion for the single-machine health state and the system-level health state.
Further, step S2 includes the following sub-steps:
s21: simulating the thermodynamic extreme test working condition of the thermal control system;
s22: and designing a spacecraft peripheral temperature sensor network, and acquiring node data by combining the internal temperature of the spacecraft to measure and obtain the limit range and the confidence probability of the temperature of the important nodes of the thermal control system under the simulated thermodynamic extreme test working condition.
Further, the step S22 specifically includes the following steps:
1) using uncertainty parameters such as flow, thermal conductance, heat source and the like as random variables X of the thermal performance of a thermal control system in the global or local area of the spacecraft;
2) simulating the temperature at the important node of the thermal control system as a system response parameter R (X);
3) calculating a plurality of groups of flow, thermal conductivity and heat source values by adopting a Monte Carlo method in combination with the simulated extreme thermodynamic test working condition, and giving a spacecraft peripheral temperature value by applying global or local thermal control system temperature simulation;
4) assuming normal temperature distribution, calculating the average value and variance of the peripheral temperature of the spacecraft according to the calculated parameters of the plurality of groups of flow, thermal conductance, heat sources and the like;
5) and obtaining the limit range and the confidence probability of the temperature at the important node of the thermal control system according to the extreme thermodynamic working condition of the thermal control system and the calculated mean value and variance of the peripheral temperature of the spacecraft.
Further, in step S21, according to the in-orbit manned and unmanned states of the spacecraft and the attitude information of the ground to the sun, the infrared cage heating and the heating sheet heating are combined to perform the external heat flow simulation of the thermodynamic extreme test working condition.
Further, step S3 includes the following sub-steps:
s31: the method for constructing the single-index parameter long-term state monitoring curve analysis model comprises the following steps: analyzing the engineering threshold of the single index parameter by adopting an engineering threshold evaluation method; analyzing the boundary interpretation of the single index parameter by adopting a limit boundary evaluation method; carrying out grading early warning on the out-of-limit or out-of-boundary single index parameters;
s32: and constructing a single index parameter characteristic model, taking the time domain characteristic and the frequency domain characteristic of the single index parameter extracted on multiple levels as a reference data column, and taking a typical working mode characteristic vector as a related data column.
Further, step S4 includes the following sub-steps:
s41: determining a flight scene characterization parameter set;
s42: determining a single index parameter, a single machine comprehensive state parameter set and a subsystem level comprehensive health state parameter set;
s43: establishing a mapping relation between the flight scene characterization parameter set and the single index parameter, the single machine comprehensive state parameter set and the subsystem level health state parameter set, and constructing a health assessment comprehensive data matrix associated with the flight scene;
s44: calculating the single index parameters of the health assessment comprehensive data matrix according to a certain characteristic statistical mode of the single index parameter characteristic model constructed in the step S32; then, calculating the correlation coefficient of a certain parameter corresponding to a certain statistical characteristic data column and a nominal characteristic data column at different comparison moments by adopting a grey correlation analysis method:
where ζ (k) is a certain characteristic parameter data column X acquired in the k-th time periodt(k) For nominal characteristic data column X0(k) The correlation coefficient of (1, 2), where m is the total amount of data segments divided according to time; alpha is a resolution coefficient;
calculating a characteristic parameter data column X by taking the correlation coefficient of the characteristic parameter data column and the nominal characteristic data column at different comparison moments as inputt(k) For nominal characteristic data column X0(k) Degree of gray correlation ri：
Further, the step S44 specifically includes the following steps:
constructing a health assessment comprehensive data matrix associated with a certain flight scene, calculating certain statistical characteristic data of a multi-level parameter assessment model according to the time domain and frequency domain characteristic statistical method of the single index parameter characteristic model constructed in the step S32, substituting and sequencing the characteristic data to obtain a certain statistical characteristic vector of the health assessment comprehensive data matrix:
[s11,s12,L,s1n,s21,L,s2m,L,s31,L,s3p] (4)
in the formula, Si,jThe parameters are elements in the feature vector, i is 1,2 and 3, represent three levels of a multi-level parameter evaluation model, and are respectively a single index parameter, a single machine comprehensive state parameter set and a subsystem level comprehensive health state parameter set; j ═ 1, 2., T, which represents the element sequence number at each level of the multi-level parameter estimation model; t is n, m, p, which respectively represents the number of elements of the first-level, second-level and third-level parameter evaluation models;
calculating q-type statistical characteristic data of the multi-level parameter evaluation model according to the time domain and frequency domain characteristic statistical method of the single index parameter characteristic model constructed in the step S32, wherein q is the total number of the time domain and frequency domain characteristic statistical method, and calculating the average gray correlation degree of each characteristic of a certain parameter and a nominal characteristic data column in a long time scene according to a formula (3); substituting the average grey correlation degree of each parameter into a health assessment comprehensive data matrix to obtain a characteristic grey correlation degree matrix:
in the formula (I), the compound is shown in the specification,
the element sequence number of the statistical characteristic representation is represented by t ═ 1, 2., q which is an element in the characteristic gray relevance matrix; the obtained characteristic gray relevance matrix represents the relevance between the multi-level parameter evaluation model of the thermal control system and an expected target in a certain flight scene, and the consistency between the data form represented by the health state parameter set of the thermal control system in the flight scene and the expected target can be judged by the score evaluation of the result of the characteristic gray relevance matrix.
The invention also provides a health parameter grading fusion diagnosis system of the thermal control system of the complex spacecraft, which comprises the following components:
the first modeling module: the system comprises a multi-level evaluation parameter model, a single-machine design parameter analysis module, a single-machine equipment health characterization module and a system-level comprehensive state characterization module, wherein the multi-level evaluation parameter model is used for constructing a multi-level evaluation parameter model of a thermal control system so as to monitor and jointly diagnose key index parameters from three aspects of single-machine design key indexes, single-machine equipment health characterization and system-level comprehensive state characterization;
a second modeling module: the method is used for designing a thermodynamic extreme working condition test data boundary model for the constructed multi-level evaluation parameter model;
a third modeling module: the single-index long-term state monitoring curve analysis model and the single-index parameter characteristic model are constructed;
a fourth modeling module: the method is used for constructing a correlation characteristic model of multiple index parameters in a space flight scene, and providing interpretation analysis and consistency comparison data for multiple indexes, single-machine comprehensive states and system-level comprehensive states.
The invention has the beneficial effects that:
1) aiming at the health state of a thermal control system of a complex spacecraft, the invention adopts index-level, single-machine-level and system-level three-level comprehensive analysis strategies to realize the rapid comparison and analysis from single parameters to single-machine equipment comprehensive parameters and finally to system-level comprehensive parameters. The method realizes the quick interpretation and the abnormal detection of the high-dimensional time sequence characteristic set, and is suitable for the intelligent auxiliary diagnosis and the quick judgment of manned spacecrafts working with/without people;
2) according to the invention, a thermodynamic extreme condition test data boundary model is designed, so that an influence model of environmental conditions of key components of a thermal control system including a pump motor and a ventilation fan motor in a fluid loop on behavior characteristics is realized, and the system-level comprehensive diagnosis capability based on an evidence theory can be improved;
3) the method establishes a mapping relation between a flight scene characterization parameter set and a system level health state parameter set, constructs a health assessment comprehensive data matrix associated with the flight scene, extracts characteristic vectors by combining a time domain and frequency domain statistical analysis method, and can determine the health states of a single index, a single level and a system level through association analysis of the characteristic vectors and a nominal characteristic matrix under the flight scene. The comprehensive credibility evaluation of the associated core indexes in the single index-single machine-system level is realized, and a discrimination method can be provided for intelligently identifying the abnormal state in the flight scene.
Drawings
FIG. 1 is a flow chart of a health parameter grading fusion diagnosis method for a thermal control system of a space station according to an embodiment of the invention;
fig. 2 is a schematic diagram of a multi-level evaluation parameter model of an active thermal control system of a space station according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings and examples, it being understood that the examples described below are intended to facilitate the understanding of the invention, and are not intended to limit it in any way. The embodiment takes a space station as an example.
As shown in fig. 1, the method for diagnosing the health parameter of the thermal control system of the space station by hierarchical fusion in this embodiment includes the following steps:
s1: and constructing a multi-level evaluation parameter model to monitor and jointly diagnose the key index parameters from three aspects of single machine design key indexes, single machine equipment health representation and system level comprehensive state representation. As shown in fig. 1, in this embodiment, taking the space station active thermal control system as an example, the space station active thermal control system is decomposed step by step according to a system level, a single level, and an index level, so as to obtain a multi-level parameter evaluation model.
S2: and designing a thermodynamic extreme condition test data boundary model for the constructed multi-level parameter evaluation model. The embodiment performs the simulation test of the external heat flow by using the combination of the infrared cage heating and the heating sheet heating according to the state that the space stands on the rail with people and without people and the attitude information of the ground to the day. The specific process of the step is as follows:
s21: thermodynamic extreme test working conditions of the thermal control system are designed and simulated according to the space station on-orbit manned/unmanned state, the flight attitude, the space station combined butt joint state and the like, and are shown in the following table 1.
TABLE 1 extreme thermodynamic test conditions for thermal control systems
S22: and designing a temperature sensor network at the periphery of the space station, and acquiring node data by combining the internal temperature of the space station, and measuring to obtain the limit range and the confidence probability of the temperature at the important node of the thermal control system under the simulated extreme thermodynamic test condition. The specific process is as follows:
1) uncertainty parameters such as flow, thermal conductance and heat source are used as random variables X of the thermal performance of the thermal control system in the global or local area of the space station;
2) simulating the temperature at the important node of the thermal control system as a system response parameter R (X);
3) calculating a plurality of groups of flow, thermal conductivity and heat source values by adopting a Monte Carlo method according to the flight state, combination state, astronaut metabolic capability and other limiting conditions of the space station, and giving a peripheral temperature value of the space station by applying global or local thermal control system temperature simulation;
4) assuming normal temperature distribution, calculating the average value and variance of the peripheral temperature of the space station according to the calculated parameters of the plurality of groups of flow, thermal conductance, heat sources and the like;
5) and obtaining the limit range and the confidence probability of the temperature at the important node of the thermal control system according to the thermodynamic extreme working condition of the thermal control system and the calculated average value and variance of the peripheral temperature of the space station.
S3: the method comprises the following steps of (1) constructing a single-index parameter long-term state monitoring curve analysis model and a characteristic model, and specifically comprising the following steps:
s31: and (5) constructing a single-index parameter long-term state monitoring curve analysis model. In this embodiment, methods such as engineering threshold evaluation and limit boundary evaluation are adopted to perform normalized monitoring and fast evaluation on aspects such as out-of-range single-index parameter, full-period envelope abnormality, and the like, and the specific process is as follows:
1) and analyzing the engineering threshold of the single index parameter. The out-of-limit state and the out-of-limit grade of a single index parameter are judged by the range constraint of each index parameter required in the parameter interpretation criterion, and once the out-of-limit of a certain single index parameter is judged, the index parameter needs to be subjected to grading early warning.
2) Analyzing the boundary interpretation of the single index parameter. And (4) obtaining thermodynamic extreme condition test data by using the thermodynamic extreme condition test data boundary model test constructed in the step (S2), and obtaining boundary conditions of various curve index parameters in different test states by analyzing the test data. According to the method, whether the curve index parameters exceed the boundary or not is judged according to the states of people and no people in orbit and the attitude information of the ground-to-day. Once it is judged that a certain index parameter exceeds the boundary, grading early warning needs to be carried out on the index parameter.
S32: and constructing a single index parameter characteristic model. Reference data is listed as X0，X0(k) The reference data sequence representing the k-th comparison time and the other data sequences as the related data sequence are marked as Xt，Xt(k) And a correlation data column indicating the k-th comparison time.
When the thermal control system calculates the degree of association between a certain telemetering parameter characteristic and a typical working mode and determines the membership of the telemetering parameter characteristic, various known typical working mode (for example, the working mode of the thermal control system listed in table 1 under 5 thermal condition scenes) characteristic vectors are selected as an associated data column, and the characteristic of the certain telemetering parameter on multiple levels is used as a reference data column.
In the operation process of the active thermal control system, for different working modes, the characteristic may be obvious only on a certain level, but the characteristic representation is not obvious on other levels, so that the telemetering parameter characteristic needs to be extracted on a plurality of levels.
Specifically, the statistical features are calculated in the time domain, as shown in table 2 below.
TABLE 2 time domain statistical characteristics and calculation formulas
In the above table, the first and second sheets,
E，x(t)，xkur，n，
xrms，C，L，xr，xv，xskes, I characterize the statistical intermediate quantity of data for a certain time period, which may also be referred to as a characteristic quantity.
And in the frequency domain, performing discrete FFT (fast Fourier transform) on the time domain telemetering data, and performing further feature extraction on the acquired amplitude-frequency characteristic curve.
When a single-index parameter characteristic model is constructed, wavelet transformation is carried out on a single-time curve to obtain an approximate coefficient and a detail coefficient on a frequency domain as frequency domain characteristics.
S4: and constructing a correlation characteristic model of the multi-element parameters in the flight scene. According to the analysis of comprehensive factors such as the diversified combination state, flight attitude, environmental factors and the like of the complex spacecraft, an associated characteristic model of multiple parameters is constructed, and interpretation analysis and consistency comparison data are provided for the multiple indexes, the single-machine comprehensive state, the system-level comprehensive state and the like. The specific process is as follows:
s41: determining a set of flight scene characterization parameters { XCombined stateXFlight attitudeXThermal environment state}，XCombined stateRepresenting diversified combination states of the combined spacecraft, such as a two-cabin one-line configuration and three-cabin flight; xFlight attitudeCharacterizing spacecraft attitude, such as + Z-axis ground or inertial attitude; xThermal environment stateCharacterizing the spatial thermal environment state, such as sun/shadow alternating external heat flow or orbital cycle average external heat flow; .
S42: and determining a multi-index parameter set, a single-machine comprehensive state parameter set and a subsystem-level comprehensive health state parameter set. The multi-index parameter set is composed of a plurality of parameters and can represent the health state of a single machine, such as ZVentilation pipeline＝{YSpeed of fan in cabinYRotating speed of circulating fan in cabinYRotational speed of cooling fan}，ZVentilation pipelineCharacterization of the health of a single ventilation duct, YSpeed of fan in cabinCharacterizing in-cabin fan speed telemetry parameters，YRotating speed of circulating fan in cabinCharacterizing the speed parameter, Y, of the circulating fan in the cabinRotational speed of cooling fanCharacterizing a rotating speed parameter of a cooling fan; a single integrated state parameter set is a parameter set composed of sets of index-level parameters, and is also an element in the integrated state parameter set of the upper-level subsystem, such as NVentilation system＝{ZVentilation pipeline}，NVentilation systemCharacterizing the comprehensive health state of the ventilation system; system-level set of integrated state parameters, such as MActive thermal control system＝{NSystem controllerNVentilation systemNFluid circuitNElectric heating temperature control}，NSystem controllerCharacterization of the health status of the system controller stand-alone, NFluid circuitCharacterization of the Single machine health of the fluid Circuit, NElectric heating temperature controlAnd (4) representing the health state of the electric heating temperature control single machine equipment.
S43: the flight scene characterization parameter set and the system level health state parameter set M are combinedActive thermal control systemEstablishing a mapping relation, and constructing a health assessment comprehensive data matrix { X) associated with a flight sceneCombined stateXFlight attitudeXThermal environment stateMActive thermal control system}。
S44: calculating the single index parameters of the health assessment comprehensive data matrix according to a certain characteristic statistical mode in the step S32; and then, calculating the correlation coefficient of a certain parameter corresponding to a certain statistical characteristic data column and a nominal characteristic data column at different comparison moments by adopting a grey correlation analysis method.
Where ζ (k) is the kth comparison time-related data sequence Xt(k) And reference data column X0(k) Is called X, and this relative difference in form is called Xt(k) To X0(k) The correlation coefficient at the kth comparison time; alpha is a resolution coefficient, and alpha is more than 0 and less than 1; if alpha is smaller, the difference between the correlation coefficients is larger, and the distinguishing capability is stronger. Preferably, α is 0.5. k is 1,2, 1, m, m is according toThe total amount of data segments divided in time.
And calculating the grey correlation degree by taking the correlation coefficients of a certain characteristic parameter data column and a nominal characteristic data column at different comparison moments as input:
in the formula, riFor a parameter characteristic data column Xt(k) For nominal characteristic data column X0(k) Gray correlation degree of (c).
Assuming that a health assessment comprehensive parameter set is constructed in a certain flight scene, calculating certain statistical feature data of a multi-level parameter assessment model according to the time domain and frequency domain feature statistical method provided in step S32, and substituting and sorting the feature data to obtain a certain statistical feature vector of a health assessment comprehensive data matrix:
[s11,s12,L,s1n,s21,L,s2m,L,s31,L,s3p] (4)
in the formula, Si,jThe parameters are elements in the feature vector, i is 1,2 and 3, represent three levels of a multi-level parameter evaluation model, and are respectively a single index parameter, a single integrated state parameter set and a subsystem integrated state parameter set. j ═ 1, 2., T, which represents the element sequence number at each level of the multi-level parameter estimation model; and T is equal to n, m and p, and respectively represents the number of elements of the first-stage, second-stage and third-stage parameter evaluation models.
According to the time domain and frequency domain feature statistical method provided by the step S32, q types of statistical feature data of the multi-level parameter evaluation model are calculated, wherein q is the total number of the time domain and frequency domain feature statistical method, and the average gray correlation degree of each feature of a certain parameter in a long time scene and a standard feature data column is calculated according to a formula (3). Substituting the average grey correlation of each parameter into a health evaluation comprehensive data matrix to obtain a grey correlation matrix:
in the formula (I), the compound is shown in the specification,
the method comprises the following steps that elements in a characteristic gray correlation degree matrix are represented, wherein i is 1,2 and 3, the three levels of a multi-level parameter evaluation model are respectively a single index parameter, a single-machine comprehensive state parameter set and a subsystem-level comprehensive health state parameter set; j ═ 1, 2., T, which represents the element sequence number at each level of the multi-level parameter estimation model; and T is equal to n, m and p, and respectively represents the number of elements of the first-stage, second-stage and third-stage parameter evaluation models. t 1, 2.. q, denotes the element number of the statistical characterization.
The obtained grey correlation matrix represents the correlation degree between the multi-stage health assessment comprehensive parameter model of the thermal control system and an expected target in a certain flight scene. By the score evaluation of the incidence matrix result, the consistency of the data form represented by the comprehensive parameter set for health evaluation of the thermal control system in the flight scene and an expected target can be judged. The method realizes comprehensive credible evaluation of the associated core indexes in the single index-single machine-system level, and also provides a discrimination method for intelligently identifying the abnormal state in the flight scene.
It will be apparent to those skilled in the art that various modifications and improvements can be made to the embodiments of the present invention without departing from the inventive concept thereof, and these modifications and improvements are intended to be within the scope of the invention.
Claims (8)
1. A health parameter grading fusion diagnosis method for a thermal control system of a complex spacecraft is characterized by comprising the following steps:
s1: constructing a multi-level parameter evaluation model of the thermal control system, and designing long-term monitoring and joint diagnosis of multi-level evaluation indexes from three aspects of single-machine key indexes, single-machine equipment health indexes and system-level comprehensive health state indexes;
s2: designing a thermodynamic extreme working condition test data boundary model for the constructed multi-level parameter evaluation model;
s3: constructing a single-index parameter long-term state monitoring curve analysis model and a single-index parameter characteristic model;
s4: and (3) constructing a characteristic correlation model of the multi-element index parameters in the flight scene, and providing an interpretation analysis method after multi-element parameter fusion for the single-machine health state and the system-level health state.
2. The method according to claim 1, wherein step S2 includes the sub-steps of:
s21: simulating the thermodynamic extreme test working condition of the thermal control system;
s22: and designing a spacecraft peripheral temperature sensor network, and acquiring node data by combining the internal temperature of the spacecraft to measure and obtain the limit range and the confidence probability of the temperature of the important nodes of the thermal control system under the simulated thermodynamic extreme test working condition.
3. The method according to claim 2, wherein step S22 is implemented as follows:
1) using uncertainty parameters such as flow, thermal conductance, heat source and the like as random variables X of the thermal performance of a thermal control system in the global or local area of the spacecraft;
2) simulating the temperature at the important node of the thermal control system as a system response parameter R (X);
3) calculating a plurality of groups of flow, thermal conductivity and heat source values by adopting a Monte Carlo method in combination with the simulated extreme thermodynamic test working condition, and giving a spacecraft peripheral temperature value by applying global or local thermal control system temperature simulation;
4) assuming normal temperature distribution, calculating the average value and variance of the peripheral temperature of the spacecraft according to the calculated parameters of the plurality of groups of flow, thermal conductance, heat sources and the like;
5) and obtaining the limit range and the confidence probability of the temperature at the important node of the thermal control system according to the extreme thermodynamic working condition of the thermal control system and the calculated mean value and variance of the peripheral temperature of the spacecraft.
4. The method of claim 2, wherein in step S21, according to the in-orbit manned and unmanned state of the spacecraft and the attitude information of the ground-to-day, the infrared cage heating and the heating plate heating are combined to simulate the extreme thermodynamic test condition by the external heat flow.
5. The method according to claim 1, wherein step S3 includes the sub-steps of:
s31: the method for constructing the single-index parameter long-term state monitoring curve analysis model comprises the following steps: analyzing the engineering threshold of the single index parameter by adopting an engineering threshold evaluation method; analyzing the boundary interpretation of the single index parameter by adopting a limit boundary evaluation method; carrying out grading early warning on the out-of-limit or out-of-boundary single index parameters;
s32: and constructing a single index parameter characteristic model, taking the time domain characteristic and the frequency domain characteristic of the single index parameter extracted on multiple levels as a reference data column, and taking a typical working mode characteristic vector as a related data column.
6. The method according to claim 5, wherein step S4 includes the following sub-steps:
s41: determining a flight scene characterization parameter set;
s42: determining a single index parameter, a single machine comprehensive state parameter set and a subsystem level comprehensive health state parameter set;
s43: establishing a mapping relation between the flight scene characterization parameter set and the single index parameter, the single machine comprehensive state parameter set and the subsystem level health state parameter set, and constructing a health assessment comprehensive data matrix associated with the flight scene;
s44: calculating the single index parameters of the health assessment comprehensive data matrix according to a certain characteristic statistical mode of the single index parameter characteristic model constructed in the step S32; then, calculating the correlation coefficient of a certain parameter corresponding to a certain statistical characteristic data column and a nominal characteristic data column at different comparison moments by adopting a grey correlation analysis method:
where ζ (k) is a certain characteristic parameter data column X acquired in the k-th time periodt(k) For nominal characteristic data column X0(k) The correlation coefficient of (1, 2), where m is the total amount of data segments divided according to time; alpha is a resolution coefficient;
calculating a characteristic parameter data column X by taking the correlation coefficient of the characteristic parameter data column and the nominal characteristic data column at different comparison moments as inputt(k) For nominal characteristic data column X0(k) Degree of gray correlation ri：
7. The method according to claim 6, wherein step S44 is implemented as follows:
constructing a health assessment comprehensive data matrix associated with a certain flight scene, calculating certain statistical characteristic data of a multi-level parameter assessment model according to the time domain and frequency domain characteristic statistical method of the single index parameter characteristic model constructed in the step S32, substituting and sequencing the characteristic data to obtain a certain statistical characteristic vector of the health assessment comprehensive data matrix:
[s11,s12,L,s1n,s21,L,s2m,L,s31,L,s3p] (4)
in the formula, Si,jThe parameters are elements in the feature vector, i is 1,2 and 3, represent three levels of a multi-level parameter evaluation model, and are respectively a single index parameter, a single machine comprehensive state parameter set and a subsystem level comprehensive health state parameter set; j ═ 1, 2., T, which represents the element sequence number at each level of the multi-level parameter estimation model; t is n, m, p, which respectively represents the number of elements of the first-level, second-level and third-level parameter evaluation models;
calculating q-type statistical characteristic data of the multi-level parameter evaluation model according to the time domain and frequency domain characteristic statistical method of the single index parameter characteristic model constructed in the step S32, wherein q is the total number of the time domain and frequency domain characteristic statistical method, and calculating the average gray correlation degree of each characteristic of a certain parameter and a nominal characteristic data column in a long time scene according to a formula (3); substituting the average grey correlation degree of each parameter into a health assessment comprehensive data matrix to obtain a characteristic grey correlation degree matrix:
in the formula (I), the compound is shown in the specification,
the element sequence number of the statistical characteristic representation is represented by t ═ 1, 2., q which is an element in the characteristic gray relevance matrix; the obtained characteristic gray relevance matrix represents the relevance between the multi-level parameter evaluation model of the thermal control system and an expected target in a certain flight scene, and the consistency between the data form represented by the thermal control system level health state parameter set in the flight scene and the expected target can be judged by the score evaluation of the result of the characteristic gray relevance matrix.
8. A health parameter grading fusion diagnosis system of a thermal control system of a complex spacecraft is characterized by comprising the following components:
the first modeling module: the system comprises a multi-level evaluation parameter model, a single-machine design parameter analysis module, a single-machine equipment health characterization module and a system-level comprehensive state characterization module, wherein the multi-level evaluation parameter model is used for constructing a multi-level evaluation parameter model of a thermal control system so as to monitor and jointly diagnose key index parameters from three aspects of single-machine design key indexes, single-machine equipment health characterization and system-level comprehensive state characterization;
a second modeling module: the method is used for designing a thermodynamic extreme working condition test data boundary model for the constructed multi-level evaluation parameter model;
a third modeling module: the single-index long-term state monitoring curve analysis model and the single-index parameter characteristic model are constructed;
a fourth modeling module: the method is used for constructing a correlation characteristic model of multiple index parameters in a space flight scene, and providing interpretation analysis and consistency comparison data for multiple indexes, single-machine comprehensive states and system-level comprehensive states.
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